RT-DETR-LGP: An Effective Defect Detection Method for Light Guide Plates via Multiscale Feature Fusion and Knowledge Distillation

Abstract

In the field of light guide plate (LGP) quality inspection in industrial production, traditional object detection models often face various challenges, such as difficulties in extracting features of small defects, low accuracy in multiscale defect detection, and interference from complex backgrounds. To effectively address these problems, this study focuses on exploring advanced object detection technologies and proposes the RT-DETR-LGP model. This model adopts the newly designed multiscale edge information enhancement (MSEIE) module and aggregate diffusion pyramid network (ADPN) module and uses a multiscale feature fusion strategy to achieve efficient detection of defects of different sizes. In addition, this study uniquely combines the model with channel-wise knowledge distillation (CWKD) technology to improve the model’s detection accuracy and generalization ability without increasing the number of model parameters. To comprehensively evaluate the performance of the RT-DETR-LGP model, 10831 LGP defect samples were carefully collected from industrial sites and used to create the industrial LGP defect dataset (ILGPDD) containing seven different types of defects. After integrating the knowledge distillation technology, the RT-DETR-LGP model demonstrated outstanding performance in all key indicators. The $\text {AP},\text {AP}_{50}$ , and ${\text {AP}}_{75}$ reached 70.3%, 98.0%, and 83.5% respectively, representing improvements of 2.5%, 1.5%, and 2.8% compared to the RT-DETR baseline network. Moreover, the model’s FPS reached 176, indicating its ability to achieve rapid LGP defect detection. These results indicate the great potential of the RT-DETR-LGP model in detecting surface defects of LGPs, providing an efficient and reliable solution for LGP quality inspection in industrial production.